September 5, 2023 | less than a minute read

Rethinking Workload Placement in The Age of Generative AI

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Richard Norris

VP Data and Digital Services

Get in touch

Richard Norris

VP Data and Digital Services

The advent of generative AI is reshaping our approach to workload placement in computing environments. No longer confined to static models of resource allocation, businesses now need to consider the dynamic nature of AI tasks.

Generative models like Chat GPT require substantial computational resources, making workload placement a critical factor for optimal performance and cost-efficiency. Companies are now looking towards flexible, hybrid cloud solutions and real-time monitoring tools to adapt to the rapidly changing demands of AI-based applications.

As the article suggests, with the complexities of performance, cost, data protection and sustainability that now have to be considered by IT, it is not surprising that 92% of the IT decision makers Dell surveyed said that they have a formal strategy for deciding where to place workloads.

Yet as IT leaders experiment with bespoke applications fueled by LLMs they must exhibit caution in how to deploy their workloads. With the calculus including such considerations as data locality and security, performance and cost efficiency, many organizations are pivoting toward an intentional approach to allocating workloads.